We will use state-of-the-art, quantitative mass spectrometry-based proteomics to define membership of protein complexes in wild-type and mutant Mtb strains under a variety of growth and perturbational conditions. Systematic and unbiased definition of the membership of key protein complexes, and the changes in that membership under a variety of growth and perturbational conditions, will provide critical insights into the biological function of genes important for growth and survival of Mtb. By integrating results of proteomics, metablomics and transcriptomic analyses, a comprehensive picture of the interactions and expression programs critical for growth and survival of the organism should emerge. We will also use state-of-the-art, quantitative global proteomics to profile auxotrophic strains of Mtb to understand the roles of genes encoding proteins and non-coding RNAs of unknown function in M. tuberculosis. We will choose conditions/time points just before changes in growth occur to minimize the possibility that changes in protein abundance are a consequence of changes in growth and survival but, instead, are directly attributable to the mutation. Both wild type and mutant bacteria strains will be analyzed by comparative quantitative proteomic analysis. We anticipate that we will find several different types of changes. We will correlate changes with observations from other experiments across the consortium.

Public Health Relevance

Protein complexes, rather than individual proteins or binary interacting proteins, are the functional units in cells. The function of any protein is context dependent, deciphering the macromolecular context in which proteins are found is key to understanding cellular function and dynamics. The systematic unbiased ID of peptide/proteins under a range of conditions will provide critical insight into the pathways and survival of Mtb

National Institute of Health (NIH)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZAI1)
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Harvard University
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Kery, Mary Beth; Feldman, Monica; Livny, Jonathan et al. (2014) TargetRNA2: identifying targets of small regulatory RNAs in bacteria. Nucleic Acids Res 42:W124-9
DeJesus, Michael A; Ioerger, Thomas R (2013) A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data. BMC Bioinformatics 14:303